A distributed optimization approach to energy management for a heavy-duty truck

Energy management systems (EMS) aim at minimizing the vehicle fuel consumption and tailpipe emissions under the wide range of driving conditions. Classical energy management systems for hybrid vehicles control the powersplit between the internal combustion engine (ICE) and the electric motor (EM) [1]. In the last decade, this research topic is extended towards integrating (thermal) battery management systems and engine aftertreatment systems (EAS). The next challenge is to extend energy management to incorporate all energy flows present in the truck [2]. Figure 1 shows the energy flows schematically for a Hybrid Truck. In this figure, there is one primary energy source, the ICE, and multiple energy converters and energy buffers. The goal is to minimize the fuel consumption whilst meeting the minimum power request of each individual energy consumer. Distributed control is a promising technique for this problem, as it will enhance modularity of the system in the sense that components can be removed or added to the system without affecting the optimality and complexity of the system. The main research questions on this topic are i) what fuel consumption reduction can be achieved by increasing the number of controlled energy flows and ii) can we develop a complete vehicle energy management system (CVEMS) that enables this with manageable complexity resulting in an acceptable development time.